Engineering Knowledge Graphs for LLM Applications

Artificial Intelligence is evolving rapidly, but even the most advanced Large Language Models (LLMs) still struggle with one critical limitation: lack of structured, reliable context. Hallucinations, inconsistent reasoning, and limited explainability continue to hold back real-world deployment.
Engineering Knowledge Graphs for LLM Applications addresses this challenge head-on.
This comprehensive, implementation-focused guide shows you how to design and integrate knowledge graphs into modern AI systems, transforming LLMs into context-aware, reliable, and explainable solutions suitable for production environments.
Rather than focusing on theory alone, this book delivers a practical, end-to-end approach to building knowledge-driven AI systems. You'll learn how to create structured data layers that serve as a single source of truth, enabling LLMs to reason more accurately and generate grounded outputs.
What You'll Learn

• How to design scalable knowledge graph architectures for LLM systems
• Principles of schema design, ontology modeling, and semantic data structures
• Techniques for entity extraction, relationship discovery, and automated graph population
• How to build and integrate Retrieval-Augmented Generation (RAG) pipelines
• Methods for multi-hop reasoning and context-aware AI workflows
• How to connect graph databases to LLM applications for real-time intelligence
• Strategies for reducing hallucinations and improving response accuracy
• Approaches to semantic search, context fusion, and knowledge-guided agents
• Best practices for scalability, performance optimization, and system design
• Governance, versioning, and production-grade deployment of knowledge-driven AI systems

• Machine Learning Engineers building LLM-powered systems
• Data Engineers and Architects working with structured data and pipelines
• AI Researchers exploring hybrid AI + knowledge systems
• Backend and Platform Engineers integrating AI into real-world applications
• Enterprise teams seeking reliable, explainable AI solutions

• Deliver more accurate and context-aware outputs
• Provide traceable and explainable reasoning
• Scale across complex, real-world data environments
• Support mission-critical and enterprise-grade applications